I am a mathematician with a focus on probability theory, statistics, and statistical learning. In addition to my experience in probability theory, I completed projects and coursework in applied machine learning, with a growing interest on AI safety.

• I hold a Bachelor's degree in Mathematics. My thesis was on Markov chain Monte Carlo methods (Metropolis-Hastings, Gibbs sampling and slice sampling) for inference in hidden Markov models.
• I also hold a Bachelor's degree in Psychology. My thesis was on agent-based modeling of behavioral phenomena in the delay-discounting paradigm.
• I am currently working on my Master's thesis in Mathematics. My thesis is on the convergence of supremum-norm test statistics and an extensions of an omnibus goodness-of-fit test for uniformity to multivariate scenarios.

I do most of my programming in Python.

Goodness-of-fit testing, empirical process theory, high-dimensional statistics, transformer architecture, TransformerLens, mechanistic interpretability, and AI misalignment.

Some of my projects are pinned below; more are in my public repositories.

Feel free to reach out: david.hamann@posteo.de.